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The MTW computation takes into account many factors that impact the throughput of WMNs, that is, the number of mesh routers, the number of mesh clients, the number of gateways, traffic dem

EURASIP Journal on Wireless Communications and Networking

Volume 2010, Article ID 368423, 12 pages

doi:10.1155/2010/368423

Research Article

On Optimizing Gateway Placement for Throughput in

Wireless Mesh Networks

Ping Zhou,1Xudong Wang,2B S Manoj,3and Ramesh Rao3

1 QCT Modem Technology Systems, Qualcomm, Inc., San Diego, CA 92121, USA

2 UM-SJTU Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China

3 Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA

Correspondence should be addressed to Xudong Wang,wxudong@ieee.org

Received 4 November 2009; Accepted 24 February 2010

Academic Editor: Xinbing Wang

Copyright © 2010 Ping Zhou et al This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

An innovative gateway placement scheme is proposed for wireless mesh networks (WMNs) in this paper It determines the location

of a gateway based on a new performance metric called multihop traffic-flow weight (MTW) The MTW computation takes into account many factors that impact the throughput of WMNs, that is, the number of mesh routers, the number of mesh clients, the number of gateways, traffic demand from mesh clients, locations of gateways, and possible interference among gateways Thus, the proposed gateway placement scheme provides a framework of significantly improving throughput of WMNs through proper placement of gateways To evaluate the performance of the new gateway placement scheme, a nonasymptotic throughput of WMNs

is derived by considering TDMA scheduling The derivations also provide a guideline for designing scheduling schemes of WMNs Numeric results show that the proposed gateway placement scheme constantly outperforms other schemes by a large margin

1 Introduction

A wireless mesh network (WMN) consists of mesh routers

and mesh clients Mesh routers form an infrastructure

network, called mesh backbone, to support the network

access of mesh clients They are powerful devices without

constraints of energy, computing power, and memory and

are usually distributed in a static and deterministic manner

WMNs offer all the advantages of ad hoc wireless networks

plus many extra benefits from the infrastructure architecture

Wireless mesh backbone can be rapidly deployed with

mini-mal cost and provides a robust, efficient, reliable, and flexible

system that supports the network access for mesh clients

Mesh backbone can also provide mesh clients with various

services and resources through their gateway and bridging

functions With infrastructure support, the complexity of

communication protocols in mesh clients can be reduced

significantly All these advantages reinforce WMNs as a

promising wireless technology for numerous applications,

for example, broadband home networking, community and

enterprise networking, public Internet access, and so on

Figure 1presents an example of a WMN in today’s digital

world

Many research problems still remain open in WMNs

challenging but problem There are some analogous research results in wired or cellular networks For example, a number

of studies have been carried out to place web proxies

Another example is the base station placement problem

replace wired links and multi-hop communications replace single-hop communications, a more comprehensive traf-fic modeling scheme is required to solve the backbone nodes placement problem in multi-hop wireless networks

multi-hop wireless networks where network nodes were partitioned into minimal number of disjoint clusters that satisfied throughput and delay constraints Various gateway

or backbone nodes placement algorithms were proposed for

focused on network connectivity of WMNs by deploying the minimum number of backbone nodes

Throughput is one of the most critical parameters that ensure the services of WMNs to meet the requirements of

Internet

accessing

gateway

Internet accessing gateway Mesh router / gateway

Mesh clients

Figure 1: A typical WMN

customers Unlike all the above research work, in this paper,

given a certain number of gateways, we aim to develop

a gateway placement algorithm to significantly enhance

throughput performance of WMNs A very similar problem

either prefixed or searched on a preselected grid in a

brutal-force way Moreover, uneven distributed traffic demand has

not been studied In our paper, optimal gateway locations can

be quickly chosen by an intelligent algorithm, which applies

for all the traffic distribution scenarios

To develop a throughput-oriented gateway placement

algorithm, we first derive a new performance metric called

multi-hop traffic-flow weight (MTW) to take into account

major factors that impact throughput of WMNs Such

factors include the number of mesh routers, mesh clients,

locations of gateways, and interference among gateways

Based on MTW, an iterative algorithm is proposed to

determine the best location of a gateway Each time a gateway

is chosen to colocate with the mesh router that has the

highest MTW

To evaluate the performance of the MTW-based

gate-way placement scheme, a throughput computation model

needs to be derived However, throughput analysis of

wireless networks is an extremely challenging research topic

Throughput capacity of multi-hop wireless networks has

derived the per-node throughput capacity for static ad

hoc networks The throughput capacity of mobile ad hoc

to WMNs, because the network architecture of WMNs is

or hybrid ad hoc networks The work of asymptotic analysis

asymptotic throughput results are obtained by assuming that the size of the network goes to infinity Since real networks always have limited size, these asymptotic results provide very limited information for practical network design Thus,

in this paper a nonasymptotic analytical model is derived

to calculate the throughput of WMNs TDMA scheduling is assumed to coordinate packet transmissions in mesh clients, mesh routers, and gateways

Numerical results based on the throughput computation model show that the new gateway placement algorithm greatly enhances the throughput performance of WMNs Comparison study is also carried out in this paper to compare the proposed scheme with other schemes such as random placement, regular placement, and busiest router placement Results illustrate that our proposed gateway placement algorithm outperforms all these schemes by a large margin

a typical WMN model is described and two throughput metrics for gateway placement are formulized The new

the throughput computation model needed by this algorithm

in Section 5 to evaluate the performance of the proposed

2 System Model and Problem Formulation

2.1 Network Topology A typical WMN model for Internet

R s j =[0,l s]2(j =1· · ·(l/ls)2), and a mesh router is placed

are more than one mesh routers and the number of mesh routers is smaller than that of mesh clients Mesh routers constitute a wireless mesh backbone providing a wireless infrastructure for mesh clients In each cell, mesh clients are connected to the mesh router like a star topology; that is,

no direct communication is available among mesh clients, and the mesh router works as a hub for mesh clients Such

is expected to be very popular in future WMN applications

that is, the number of gateways cannot exceed the number

of mesh routers We chose the square grid topologies mainly

have shown that square grid topologies are more realistic in delivering the desired network performance

Each mesh client is a data source and a data destination All mesh clients are equivalent such that they always have the same amount of packets to send or receive during a certain time Unlike mesh clients, mesh routers are neither

Mesh router with gateway function

Mesh router without gateway function

Mesh client

l

l s

Figure 2: Network topology of an infrastructure WMN with

gateways

data source nor data destination; they only route and forward

data for mesh clients All traffic is assumed to go through

gateways Each mesh router is associated with its nearest

gateway such that it relays packets to or from it Assuming

that the shortest path routing is applied, the nearest gateway

of a mesh router is defined as the gateway that the mesh

router can access to by the minimal number of hops In

the situation that a mesh router has more than one nearest

gateways by round robin A mesh client is said to be

associated with a gateway if its connected router is associated

be shared by all its potentially associated gateways

In this paper the following definitions of

communica-tions will be frequently used

(i) Local communications: it is referred as the

communi-cations between a mesh router and a mesh client

(ii) Backbone communications: it is referred as the

communications between two mesh routers, which

includes the communications between a gateway and

a mesh router

(iii) Downlink communications: it is referred as the

com-munications from a gateway to a mesh client, in

which a data packet is first relayed among mesh

routers in backbone communications and is then sent

by a mesh router to one of its connected mesh clients

(iv) Uplink communications: it is referred as the

commu-nications from a mesh client to a gateway, in which

a data packet is sent in the exact reverse direction as

described in downlink communications

2.2 Transmission Model To help elaborate the new gateway

placement scheme and its throughput computation, a

trans-mission model is specified as follows

Each mesh router is equipped with two radio interfaces:

so that local communications do not interfere with backbone communications It should be noted the two radio interfaces

of a mesh router can be two physical radio interfaces or two virtual radio interfaces In the later case, only one physical radio interface is needed for a mesh router and switching channels in time slots for backbone or local communications achieves two virtual interfaces

Moreover, mesh routers can receive packets from only one sender at a time The same constraint is imposed on mesh clients Transmission and reception can occur in either time-division duplex (TDD) or frequency division duplex (FDD), depending on how the physical and MAC layers are implemented

In either local communications or backbone communi-cations, simultaneous transmissions are coordinated by the

correspond, respectively, to the transmission range of node

guard zone in the Protocol Model

2.3 Throughput In order to evaluate the performance of

gateway placement algorithms, the aggregate throughput and the worst-case per-client throughput need to be derived In this subsection, two problems of throughput maximization are formulized, which leads to the definitions of two throughput metrics The actual framework of computing the nonasymptotic value of these throughput metrics will be

Problem 1 Optimal gateway placement for maximizing aggre-gate throughput of WMNs, that is, in the above WMN model,

routers’ distribution, transmission, scheduling and routing

such that

Nc



i =1

i, N g



(1)

deployed

Problem 2 Optimal gateway placement for maximizing the worst-case per-client throughput in the WMN, that is, in the

clients’ distribution, routers’ distribution, transmission, and

Nc

min

i =1 TH

i, N g



(2)

is maximized

3 Multihop Traffic-Flow Weight

Gateway Placement

Adding new gateways can increase throughput in backbone

communications by effectively reducing the average number

of hops each packet needs to access to gateways and reducing

the traffic load on existing gateways However, the above

benefits can dramatically diminish due to inappropriate

gateway placement, since new gateways will also result in

more interference to existing gateways Therefore, the best

load in the network but also introduce minimal interference

In general a gateway placement scheme must be adaptive

to the deployed number of gateways A relative small number

of deployed gateways mean a large number of hops that a

packet needs to traverse to gateways, which results in huge

traffic load Therefore, geometry-balanced placement

algo-rithms, for example, regular placement, may achieve fairly

good results since they can effectively reduce the average

number of hops In the opposite case, when a relatively large

number of gateways are planned for deployment, placing the

gateways in the areas with the most traffic load may be simply

the best solution

In this section, an innovative gateway placement

algo-rithm is proposed It holds all the above-mentioned benefits

In the algorithm, a traffic-flow weight, denoted by MTW(j),

Each time a new gateway will be placed on the router with

the highest weight The weight computation is adaptive to

the following factors: (1) the number of mesh routers and the

from mesh clients; (3) the location of existing gateways in

the network; and (4) the interference from existing gateways

How factors (1) to (3) are captured in MTW will be discussed

inSection 3.1, and the relationship between factor (4) and

gateway placement algorithm will be explained while the

3.1 Adaptive Multihop Traffic-Flow Weight In the first step

of the algorithm, a variable called gateway radius, denoted by

⎛

⎝ N r

N g

⎞

The rationale of this estimation can be explained as follows

N r /

N r /2

its farthest mesh router It should be noted that (1) only

provides an estimation, which may not be always precise for

3

5

5 7

10

10 10

11

8

8 8 8

9

9 4

9 9

9

(a)

261 201 222 212

237 293

265

165

218 266 284

(b)

Figure 3: An example of multi-hop traffic-flow weight

In the second step, local traffic demand on each mesh

D( j) is actually the traffic demand from all the mesh clients

uniformly distributed and 25 mesh routers are placed on a 5-by-5 regular grid

as follows:

j

=R g+ 1

× D

j

R g −1

R g −2

+· · ·

(4)

placement according to MTW In this example, there is only

will be placed in the center mesh router of the WMN that has the highest MTW weight

If more than one gateway is to be placed, two additional

-hop neighbors will be reduced to half In this way, another gateway is less likely to be placed in a location near the existing gateway Secondly, interfere among gateways should

be counted in the computation of MTW, as discussed in the next subsection

(a)

(1) (2)

(3)

(4)

1

(b)

0.625

0.25 0.375

(c)

Time slot number 1: 136 Time slot number 2: 27 Time slot number 3: 14 Time slot number 4: 156 Time slot number 5: 27 Time slot number 6: 14 Time slot number 7: 136 Time slot number 8: 25

(d)

Figure 4: Obtaining the optimal sharing efficiecy on gateways

3.2 Sharing Efficiency of Gateways Two gateways interfere

Interfering gateways have to share the same wireless channel

in the backbone communications An algorithm is developed

The algorithm holds the two distinct features: (1) full fairness

among gateways will be guaranteed; (2) under the condition

In the first step, the table of nonoverlapping interfering

groups is constructed as follows: (1) each interfering group

appears as a single row in the table and contains a set of

gateways, any two of which interfere with each other; (2) the

group with more gateways always has a smaller row number,

that is, it appears earlier in the table; (3) a group appearing

later must have at least one gateway that is not included by all

the previous groups For example, seven gateways deployed

Table 1

In the second step, each gateway is assigned a percentage

value according to the following procedure (1) Initially all

gateways are assigned with a value of 100% (2) The table of

non-overlapping interfering groups is searched from the top

row to the last row at a pace of one row per step (3) In each

step, all gateways in a specific row are split into 2 groups by

a threshold value of (1/the number of gateways in the row)

The first group contains the gateways with a larger value than

the threshold and the second group contains the rest of the

gateways in this row (4) All gateways in the first group will then be reassigned a new percentage value calculated as

(5)

if the new one is smaller than its current value (5) The procedures of (3) and (4) are repeated until the end In

4, 5, and 7 are reassigned a percentage value of 25% in the computation of the first row; gateway 2 is reassigned a percentage value of 50% in the computation of the second row; gateways 2 and 6 are reassigned a percentage value of 37.5% in the computation of the third row; gateway 1 is reassigned a percentage value of 62.5% in the computation

The final percentage value assigned to each gateway in the

guar-antees a full fairness among all the gateways, and secondly

scheme for all the gateways, since in each interfering group,

than 100% In the scheduling scheme, time slots in backbone communications are assigned to all gateways such that successful simultaneous transmissions can be always carried out in each time slot Each gateway can be guaranteed to have

a number of time slots, which is equal to the total number

shows a TDMA scheduling scheme for the above example

By taking into account the interference of gateways via the sharing efficiency, a new gateway can be placed into the network with the following procedures: (1) from

the highest weight as a potential location for gateway placement; (2) reconstructing the table of non-overlapping interfering groups by adding the potential location into the

potential gateway location; (4) readjusting the highest weight

MTW(j) × Ge ff j); and (5) if the new weight is still larger

than the second highest weight, then place the gateway in the location otherwise, repeat the above steps from (1) to (5) until obtaining the location

4 Traffic Scheduling for Throughput Computation

In this section, a TDMA scheme is applied for traffic scheduling One key benefit of using TDMA is that it guarantees collision free transmissions In fact, various TDMA scheduling schemes are actually used in a few wide area wireless mesh network testbeds and network standards such as WiMAX Based on TDMA scheduling, we provide

a framework of non-asymptotic throughput derivation for WMNs

The WMN model indicates that all wireless mesh routers

Table 1: Optimal sharing efficiency calculation.

Row no Non-overlapping interfering group Sharing efficiency

backbone communications, and all mesh routers and mesh

i, N g



=min THW1



i, N g



, i =1· · · N c

(6)

mesh client in backbone communications when there are

(2) indicates that a feasible per-client throughput can be

downlink communications, respectively, it is assumed that

1 Generally, throughput of a mesh client should be obtained

as the sum of uplink throughput and downlink throughput

actual applications running on clients, which is beyond the

objectives of this paper It is assumed in the following of

communications and throughput is decided by downlink

is not an uncommon case in today’s applications of WMNs,

for instance, in the application of Internet access We shall

note that the methodology proposed in this section can

actually be used to obtain throughput of WMNs when both

uplink traffic and downlink traffic are present However,

with the above simplified model, we can focus on the

illustration of the key ideas without being distracted by trivial

discussions

4.1 Throughput in Backbone Communications Time slots

in backbone communications are first assigned to gateways

so that no gateways interfere with each other The TDMA scheduling scheme on gateways is assumed to satisfy the following two conditions: (1) time slots are assigned to each gateway with full fairness; (2) under the condition of (1), each gateway should have as much as possible time slots

obtain the optimal sharing efficiency on all the gateways,

scheduling scheme satisfying the above two conditions is

guaranteed to have a number of time slots, which is equal

kth gateway is guaranteed to have an aggregate throughput of

scheme, interfering gateways share the same wireless channel while noninterfering gateways can transmit simultaneously

In the next step, time slots of a gateway will be further split into small time slots to have the following two proper-ties: (1) each mesh client associated with the specific gateway should have separate small time slots for “interference free” transmissions; (2) each of such mesh clients should achieve

a common throughput in backbone communications, that

are associated with the same gateway It is assumed that a

small time slots if there are no simultaneous transmissions

than SRD-hops away from its gateway SRD is defined as Slot Reuse Distance, for instance, SRD =3 inFigure 5 Therefore,

Nhop

j

= Nhop

j

j

< SRD;

Nhop

j

=SRD, ifNhop

j

With the first property all mesh clients associated with

time slots for “interference free” transmissions in backbone

communications Hence, the kth gateway can guarantee the

R j

1

2 3

3

Figure 5: A TDMA scheduling scheme in backbone

communica-tions with SRD=3

following per-client throughput for all its associated mesh

clients in backbone communications:



l =all associated routers with thekth gateway



N C(l)× Nhop (l).

(8)

With the consideration that a mesh router may have more

than one potentially associated gateways and it will use all

these gateways by round robin for fairness, the mesh router

will assign all its time slots equally to its associated gateways

Therefore, the per-client throughput on the kth gateway can

be modified to



l =all associated routers

with thekth gateway



N C(l)× Nhop (l)÷ N g(l),

(9)

Assuming that the ith mesh client is connected with the

mesh client in backbone communications is the averaged

throughput over all its associated gateways:



i, N g



=



k =all the associated gateways with the mesh routerR j

N g

computation in backbone communications In the example,

there are 5 mesh routers, 2 of which are also gateways,

have 50% sharing efficiency and all the mesh routers have

robin Thus, we have

N C(1)= N C(2)= N C(3)=10,

Nhop (1)= Nhop (2)= Nhop (3)=1,

N g(1)= N g(3)=1, N g(2)=2

(11)

R1 G1 R2 G2 R3

Figure 6: An example of traffic scheduling in backbone communi-cations

communica-tions

the full fairness among mesh clients for each gateway Note that farther mesh clients from gateways are reserved more time slots for transmission so that their throughput is not throttled by closer ones

The per-client throughput in backbone communications will be compared with the per-client throughput in local communications to decide the per-client throughput in the WMN Note that if a mesh client is connected directly to

a gateway, its throughput is decided only by the per-client throughput in local communications

4.2 Throughput in Local Communications Separate time

simultaneous transmissions can only be carried out in cells that have enough distance in between; that is, simultaneous

apart, where CRF is defined as Cell Reuse Factor Hence, in

downlink communications, each mesh router can only have

The above slot is further split into separate small-slots Being assigned a different small-slot, each mesh client is guaranteed to obtain successful reception from its associated mesh router Therefore,

j, i =1· · · N c (13)

With the above TDMA scheme, all the mesh clients associated with the same mesh router will have the same

mesh router

1 2

Figure 7: A TDMA scheduling scheme in local communications

with CRF=4

4.3 Feasible Throughput in WMN Combining (6)–(13), a

feasible non-asymptotic throughput of the ith mesh client in

the WMN can be obtained as follows:

i, N g



⎧

⎪

⎪

⎪

⎪

⎛

⎜

⎜

⎜



k =all the associated gateways

with the mesh routerR j



l =all associated routers

with thekth gateway



N C(l)× Nhop (l)÷ N g(l)

⎞

⎟

⎟

⎟

/N g

j

j

⎫

⎪

⎪

⎪

⎪

,

(14)

and here ith mesh client is assumed to be connected with

non-asymptotic throughput estimation is more realistic than the

asymptotic throughput that is estimated when the number of

nodes approaches infinity

When all mesh routers are chosen as gateways, that

aggregate throughput:

Nc



i =1

i, N g



≤

Nc



i =1

= c2W2

Nr



j =1

u

j

,

(15)

10

10 30

30 30

60

Figure 8: An example of uneven nodes’ distribution

obtained for the worst-case per-client throughput:

min

i TH

i, N g



i THW2(i)

j

c2W2

j

j

(16)

It should be noted that the throughput computation method is applicable to any gateway placement algorithm; that is, as long as a gateway placement is given, the results derived in this section can be used to calculate the throughput of WMNs

5 Numeric Results and Discussion

Using the framework of throughput computation derived

inSection 4, throughput of this WMN is studied In all the

that is, there are 200 mesh clients distributed in a square

small square cells and a mesh router is placed in the center

Comparison study is conducted between the proposed algorithm (MTWP) and the other three gateway placement algorithms

1· · · N

16 14 12 10 8 6 4 2

0

The number of gateways Upper bound

W1=10

W1=15

W1=20

W1=25

0

1

2

3

4

5

6

7

8

9

×10 7

Figure 9: The aggregate throughput by changing the number of

gateways with different channel capacity of mesh routers

10 8

6 4

2

0

The number of gateways Upper bound

MTWP

RDP

BRP RGP

0

1

2

3

4

5

6

7

8

9

×10 7

Figure 10: The comparison of the aggregate throughput with

uniformly distributed mesh clients

(iii) Regular Placement (RGP): as many as possible

gate-ways are placed based on regular patterns and the

rest of them choose their placement location on the

an example of RGP on a 6-by-6 regular grid

Given a certain placement algorithm, a number of

gate-ways will be placed on the top of the best-fit mesh routers

For each algorithm, per-client throughput is calculated based

10 8

6 4

2 0

The number of gateways Upper bound

MTWP RDP

BRP RGP

0

0.5

1

1.5

2

2.5

×10 5

Figure 11: The comparison of the worst-case per-client throughput with uniformly distributed mesh clients

worst-case per-client throughput are obtained as described in

Section 2.3 The upper bounds of the above two throughputs

mesh clients in all cases follow a random distribution, the results in all plots are obtained as an average over 200 iterations

In the first case, we study the relationship between channel capacity of mesh routers and the number of gateways We assume that all mesh clients are uniformly distributed and each of them can transmit at 10 Mbps in

aggregate throughput of the WMN versus the number of

by the proposed MTWP algorithm and the channel capacity

of mesh routers varies from 10 Mbps to 25 Mbps with

an increment of 5 Mbps Our results confirm the fact that the number of gateways can be dramatically reduced

by using more powerful mesh routers in the backbone; for example, 6 gateways with mesh router transmitting

at 25 Mbps can achieve much better throughput perfor-mance than 15 gateways with mesh router transmitting at

10 Mbps

compare throughput performance of four gateway place-ment algorithms in the WMN We assume that all mesh clients are uniformly distributed and each mesh client

and mesh router can transmit at 10 Mbps and 20 Mbps,

respectively The results show that the proposed MTWP algorithm clearly outperforms the other algorithms in both the aggregate throughput and the worst case throughput The regular placement algorithm achieves the second best results because it is a geometry-balanced algorithm which

Table 2: An example for RGP on a 6-by-6 regular grid.

N g Gateway placement

1 Choose the busiest router from the location of (3,3), (3,4), (4,3), and (4,4)

2∼4 Choose theN gbusiest routers from the location of (2,2), (2,5), (5,2), and (5,5)

5∼7 Choose the first 4 gateways at the location of (2,2), (2,5), (5,2), and (5,5) and choose the rest on the other

routers with the highest traffic demand 8

36 routers are split into 4 groups In each group, any two routers are at least 2-hops away, for example, (1,1), (1,3), (1,5), (3,2), (3,4), (3,6), (5,1), (5,3), and (5,5) are in one group Choose the first gateway on the busiest router and choose the rest 7 gateways on the next 7 busiest routers in the same group with the first one

≥9

36 routers are split into 4 groups as above Choose the first gateway on the busiest router, then choose the next 8 gateways on the other routers in the same group with the first one, and choose the rest on the other routers with the highest traffic demand

16 14 12 10 8 6 4 2

0

The number of gateways Upper bound

MTWP

RDP

BRP RGP

0

2

4

6

8

10

12

14

16

18×10 7

Figure 12: The comparison of the aggregate throughput with

unevenly distributed mesh clients

and its associated mesh routers

compare throughput performance of four gateway

place-ment algorithms when mesh clients are distributed unevenly

distributed; however, nodes density is very different among

the nine regions In this case, MTWP algorithm outperforms

the other three algorithms in every single case Here we

double the channel capacity of mesh clients assuming

that mesh clients and mesh routers can both transmit at

20 Mbps Otherwise, improvements by gateway placement

algorithms may not be observed since very low throughput

of local communications becomes the major constraint for

throughput performance of the whole WMN, which results

from very high node density in some regions

In both the second and third cases, as shown in

16 14 12 10 8 6 4 2 0

The number of gateways Upper bound

MTWP RDP

BRP RGP

0

0.5

1

1.5

2

2.5

3

×10 5

Figure 13: The comparison of the worst-case per-client throughput with unevenly distributed mesh clients

improvement in throughput when the number of gateways

is chosen from five to eight An explanation is given

as follows: with more than four gateways in a 6-by-6 grid backbone network, gateways start to interfere with each other Comparing with the other three algorithms, MTWP algorithm has a unique mechanism to mitigate such interference among gateways Thus, countering interference among gateways is very critical for a gateway placement algorithm

An important problem that WMN service providers face is the deployment cost involved in setting up the gateways Thus, a performance metric to evaluate the cost

of a gateway placement algorithm can be the aggregate

results indicate that there exist an optimal number of gateways that achieve best tradeoff between the gateway cost and throughput More importantly, it is illustrated that

potential gateway location; (4) readjusting the...

Figure 4: Obtaining the optimal sharing efficiecy on gateways

3.2 Sharing Efficiency of Gateways Two gateways interfere

Interfering gateways have to share the same wireless channel... the following of

communications and throughput is decided by downlink

is not an uncommon case in today’s applications of WMNs,

for instance, in the application of Internet